Work arrangement: Remote. Open to candidates located in South America and Central America only.

About The Company

The company is building an institutional intelligence platform for Canadian public mining companies.

The platform organizes regulatory filings, technical reports, insider records, news releases, company websites, and financial disclosures into structured, searchable information. Investors, analysts, and investor-relations teams can ask questions and receive grounded answers with references to the original source material.

The company is currently includes a production-oriented embeddable investor experience, a structured financial and technical-data platform, and an authenticated analyst and IR application under active development.

The Role

We are looking for a hands-on Senior Backend / Data Platform Engineer to own and strengthen the systems that make Invexis accurate, secure, reliable, and scalable.

This is not a generic CRUD role. You will work across backend APIs, document ingestion, structured data, search and retrieval, AI/LLM-enabled workflows, security, observability, and production operations.

The ideal candidate is comfortable working in an early-production environment: improving what exists, identifying the next constraint, and introducing pragmatic infrastructure as the product and customer base grow.

What You’ll Work On 

• Design and maintain backend services and APIs supporting the Invexis product. 
• Build and improve pipelines that collect, process, normalize, and update large volumes of public-company information. 
• Evolve structured data models for financial statements, capital structure, financings, insider activity, management, mining projects, technical reports, risks, catalysts, and disclosures. 
• Build reliable AI/LLM-enabled workflows that produce useful, well-grounded results. 
• Preserve source attribution and data integrity from ingestion through user-facing answers. 
• Design secure, tenant-isolated services for issuer websites and internal users.
• Improve database performance, indexing, transaction safety, connection management, and query reliability. 
• Introduce durable asynchronous processing, including queues, retries, failure handling, dead-letter workflows, and replay capabilities. 
• Improve resilience as the system grows from a single-node foundation toward multiple processes, services, or hosts where appropriate. 
• Develop shared rate limits, tenant quotas, usage controls, and AI cost monitoring. 
• Build operational dashboards, alerts, diagnostics, and runbooks for latency, errors, data freshness, provider failures, and system health.
•  Strengthen deployment, rollback, backup, and disaster-recovery practices. 
• Write comprehensive automated tests, regression fixtures, integration checks, and AI evaluation tooling. 
• Participate in technical planning, incident response, and architectural decision-making.

First 90 Days - You will be expected to:

• Understand the product, data model, ingestion lifecycle, query flow, and operational boundaries. 
• Trace a user question from request handling through structured data or retrieval to the final cited response. 
• Ship at least one meaningful improvement to backend reliability, data correctness, security, or observability. 
• Identify the most important near-term scaling and operational risks. 
• Improve tests, monitoring, documentation, or runbooks in an area you own. 
• Establish a clear technical plan for the next stage of backend and data-platform maturity.

First 6–12 Months - Success will include:

• A more resilient and observable backend. 
• More reliable ingestion and source processing. 
• Better handling of asynchronous work, retries, replay, and provider failures. 
• Improved database performance and safer behaviour under concurrency. 
• Stronger tenant isolation, usage controls, and AI cost visibility. 
• A clearer path from the current early-production architecture to a scalable multi-tenant platform. 
• Backend foundations that support the expanding analyst, IR, analytics, and integration roadmap.

What We’re Looking For

• 5+ years of professional software engineering experience, with substantial ownership of production backend or data systems. 
• Strong experience with Node.js, JavaScript, TypeScript, or a comparable backend language. 
• Strong SQL and relational database fundamentals, including indexes, transactions, connection pools, and query analysis.
• Experience building ingestion pipelines, data synchronization, asynchronous job, or other systems that process and organize large volumes of data. 
• Experience operating services in production and reasoning through incidents, bottlenecks, and failure modes. 
• Practical understanding of authentication, authorization, multi-tenancy, API security, and data isolation. 
• Experience designing reliable systems with idempotency, observability, retries, and recovery workflows. 
• Fluency with AI/LLM concepts and practical experience building applications that use LLMs, retrieval, structured generation, or similar techniques. 
• Strong testing and debugging habits. 
• Ability to make sound architectural decisions in an evolving product without requiring every detail to be specified in advance. 
• Clear written and verbal communication, strong judgment, and a high degree of ownership.

Nice to Have 

• Experience processing PDFs, regulatory filings, financial data, or technical documentation. 
• Experience with mining, capital markets, investor relations, accounting, or public-company data. 
• Experience building search, knowledge-management, document-intelligence, or retrieval systems. 
• Experience with cloud services, containers, Linux-based production systems, CI/CD, and monitoring. 
• Experience designing usage controls, cost controls, or provider-abstraction layers for AI-enabled products. 
• Experience building evaluation systems for AI quality, hallucination prevention, prompt-injection resistance, or source attribution. 
• Familiarity with modern web applications and frontend/backend collaboration. 
• French-language capability is helpful but not required.

Technical Environment

The role involves backend services, APIs, relational data, document-processing pipelines, search and retrieval systems, AI/LLM integrations, cloud-hosted infrastructure, automated deployments, authenticated web applications, and an embeddable browser experience.

We value strong engineering fundamentals and practical production judgment over experience with any specific vendor, framework, cloud provider, or infrastructure trend.

What Success Looks Like 

You help make the product a system that customers and internal teams can trust: accurate information, traceable sources, secure tenant boundaries, predictable performance, clear operational visibility, and a backend that can grow without unnecessary complexity.